The Study of Improved FP-Growth Algorithm in MapReduce
Authors
Sun Hong, Zhang Huaxuan, Chen Shiping, Hu Chunyan
Corresponding Author
Sun Hong
Available Online November 2013.
- DOI
- 10.2991/ccis-13.2013.58How to use a DOI?
- Keywords
- FP-Growth, IFP algorithm, MapReduce Introduction (Heading 1)
- Abstract
As FP-Growth algorithm generates a great deal of conditional pattern bases and conditional pattern trees, leading to low efficiency, propose an improved FP-Growth(IFP) algorithm which firstly combine the same patterns based on the situation whether the support of the transaction is greater than the minimum support(min_sup) to mine the frequent patterns. Thus the IFP cuts down on the space and improves the efficiency. It also makes it easy to be paralleled. Further more, combine the IFP algorithm with the MapReduce computing model, named MR-IFP(MapReduce-Improved FP), to improve the capability to deal with the mass data.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Sun Hong AU - Zhang Huaxuan AU - Chen Shiping AU - Hu Chunyan PY - 2013/11 DA - 2013/11 TI - The Study of Improved FP-Growth Algorithm in MapReduce BT - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security PB - Atlantis Press SP - 250 EP - 253 SN - 1951-6851 UR - https://doi.org/10.2991/ccis-13.2013.58 DO - 10.2991/ccis-13.2013.58 ID - Hong2013/11 ER -